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Automatic Creation of HumanCompetitive Programs and Controllers by Means of Genetic Programming
, 2000
"... Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where genetic programming has previously produced humancompetitive results. It then presents new humancompeti Z. tive results involv ..."
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Cited by 35 (16 self)
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Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where genetic programming has previously produced humancompetitive results. It then presents new humancompeti Z. tive results involving the automatic synthesis of the design of both the parameter values i.e., tuning and the topology of controllers for two illustrative problems. Both genetically evolved controllers are better than controllers designed and published by experts in the field of control using the criteria established by the experts. One of these two controllers infringes on a previously issued patent. Other evolved controllers duplicate the functionality of other previously patented controllers. The results in this paper, in conjunction with previous results, reinforce the prediction that genetic programming is on the threshold of routinely producing humancompetitive results and that genetic programming can potentially be used as an "invention machine" to produce patentable new inventions.
Evolution of a Controller with a Free Variable Using Genetic Programming
 Genetic Programming, Proceedings of EuroGP'000, volume 1802 of LNCS
, 2000
"... A mathematical formula containing one or more free variables is "general" in the sense that it provides a solution to an entire category of problems. For example, the familiar formula for solving a quadratic equation contains free variables representing the equation's coefficients. Previous work has ..."
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Cited by 4 (0 self)
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A mathematical formula containing one or more free variables is "general" in the sense that it provides a solution to an entire category of problems. For example, the familiar formula for solving a quadratic equation contains free variables representing the equation's coefficients. Previous work has demonstrated that genetic programming can automatically synthesize the design for a controller consisting of a topological arrangement of signal processing blocks (such as integrators, differentiators, leads, lags, gains, adders, inverters, and multipliers), where each block is further specified ("tuned") by a numerical component value, and where the evolved controller satisfies userspecified requirements. The question arises as to whether it is possible to use genetic programming to automatically create a "generalized" controller for an entire category of such controller design problems # instead of a single instance of the problem. This paper shows, for an illustrative problem, how genetic programming can be used to create the design for both the topology and tuning of controller, where the controller contains a free variable. 1
Using Fitness Distributions to Improve the Evolution of Learning Structures
, 1999
"... In this paper, the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operatoradaptation, where the optimization of density estimation models serves as an example. A new infor ..."
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Cited by 4 (2 self)
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In this paper, the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operatoradaptation, where the optimization of density estimation models serves as an example. A new information theory based measure is proposed to judge the accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic programming. An efficient method to calculate the gradient information is presented. 1 Introduction In this paper we discuss the usefulness of judging evolutionary operators by their absolute benefit, a measure of improvement in the fitness space. Sections 2 and 3 motivate the absolute benefit from the viewpoint of fitness distribution and fitness trajectory analysis, respectively. Two example applications of the absolute benefit are given. Both are structure optimization...
Evolving Stochastic Processes Using Feature Tests and Genetic Programming
, 2009
"... The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest vary over time in a probabilistic, and often noisy, manner. A suite of statistical feature tests are performed on time ser ..."
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Cited by 2 (2 self)
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The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest vary over time in a probabilistic, and often noisy, manner. A suite of statistical feature tests are performed on time series plots from example processes, and the resulting feature values are used as targets during evolutionary search. A process algebra, the stochastic πcalculus, is used to denote processes. Investigations consider variations of GP representations for a subset of the stochastic πcalculus, for example, the use of channel unification, and various grammatical constraints. Target processes of varying complexity are studied. Results show that the use of grammatical GP with statistical feature tests can successfully synthesize stochastic processes. Success depends upon a selection of appropriate feature tests for characterizing the target behaviour, and the complexity of the target process.
The Evolution of Higherlevel Biochemical Reaction Models
, 2010
"... Computational tools for analyzing biochemical phenomena are becoming increasingly important. Recently, highlevel formal languages for modeling and simulating biochemical reactions have been proposed. These languages make the formal modeling of complex reactions accessible to domain specialists outs ..."
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Cited by 2 (1 self)
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Computational tools for analyzing biochemical phenomena are becoming increasingly important. Recently, highlevel formal languages for modeling and simulating biochemical reactions have been proposed. These languages make the formal modeling of complex reactions accessible to domain specialists outside of theoretical computer science. This research explores the use of genetic programming to automate the construction of models written in one such language. Given a description of desired timecourse data, the goal is for genetic programming to construct a model that might generate the data. The language investigated is Kahramano˘gullari’s and Cardelli’s PIM language. The PIM syntax is defined in a grammarguided genetic programming system. All time series generated during simulations are described by statistical feature tests, and the fitness evaluation compares feature proximity between the target and candidate solutions. Target PIM models of varying complexity are used as target expressions for genetic programming. Results were very successful in all cases. One reason for this success is the compositional nature of PIM, which is amenable to genetic program search.